Algorithm for automated selection of application-specific fiber-optic reflectance probes

• Published in: Journal of biomedical optics Vol. 18; no. 2; p. 027012
• Main Authors: Gomes, Andrew J, Backman, Vadim
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.02.2013
• Subjects: Algorithms; Barrett Esophagus - diagnosis; Colonic Neoplasms - blood; Colonic Neoplasms - diagnosis; Computer Graphics; Fiber optics; Hemoglobins - metabolism; Humans; Light; Optical fibers; Optical Fibers - statistics & numerical data; Optical Phenomena; Optical properties; Optimization; Reflectance; Reflectivity; Research Papers: Sensing; Sampling; Scattering, Radiation; Software; Spectroscopy; Spectrum Analysis - methods; Spectrum Analysis - statistics & numerical data; optical design; biophotonics; biomedical optics; reflectance probes
• ISSN: 1083-3668; 1560-2281; 1560-2281
• DOI: 10.1117/1.JBO.18.2.027012

Several optical techniques and fiber-optic probe systems have been designed to measure the optical properties of tissue. While a wide range of options is often beneficial, it poses a problem to investigators selecting which method to use for their biomedical application of interest. We present a methodology to optimally select a probe that matches the application requirements. Our method is based both on matching a probe's mean sampling depth with the optimal diagnostic depth of the clinical application and on choosing a probe whose interrogation depth and path length is the least sensitive to alterations in the target medium's optical properties. Satisfying these requirements ensures that the selected probe consistently assesses the relevant tissue volume with minimum variability. To aid in probe selection, we have developed a publicly available graphical user interface that takes the desired sampling depth and optical properties of the medium as its inputs and automatically ranks different techniques in their ability to robustly target the desired depth. Techniques investigated include single fiber spectroscopy, differential path length spectroscopy, polarization-gating, elastic light scattering spectroscopy, and diffuse reflectance. The software has been applied to biological case studies.